Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model.

In this study, a wearable inertial measurement unit system was introduced to assess patients via the Berg balance scale (BBS), a clinical test for balance assessment. For this purpose, an automatic scoring algorithm was developed. The principal aim of this study is to improve the performance of the machine-learning-based method by introducing a deep-learning algorithm. A one-dimensional (1D) convolutional neural network (CNN) and a gated recurrent unit (GRU) that shows good performance in multivariate time-series data were used as model components to find the optimal ensemble model. Various structures were tested, and a stacking ensemble model with a simple meta-learner after two 1D-CNN heads and one GRU head showed the best performance. Additionally, model performance was enhanced by improving the dataset via preprocessing. The data were down sampled, an appropriate sampling rate was found, and the training and evaluation times of the model were improved. Using an augmentation process, the data imbalance problem was solved, and model accuracy was improved. The maximum accuracy of 14 BBS tasks using the model was 98.4%, which is superior to the results of previous studies.

Building Linked Big Data for Stroke in Korea: Linkage of Stroke Registry and National Health Insurance Claims Data.

BACKGROUND: Linkage of public healthcare data is useful in stroke research because patients may visit different sectors of the health system before, during, and after stroke. Therefore, we aimed to establish high-quality big data on stroke in Korea by linking acute stroke registry and national health claim databases. METHODS: Acute stroke patients (n = 65,311) with claim data suitable for linkage were included in the Clinical Research Center for Stroke (CRCS) registry during 2006-2014. We linked the CRCS registry with national health claim databases in the Health Insurance Review and Assessment Service (HIRA). Linkage was performed using 6 common variables: birth date, gender, provider identification, receiving year and number, and statement serial number in the benefit claim statement. For matched records, linkage accuracy was evaluated using differences between hospital visiting date in the CRCS registry and the commencement date for health insurance care in HIRA. RESULTS: Of 65,311 CRCS cases, 64,634 were matched to HIRA cases (match rate, 99.0%). The proportion of true matches was 94.4% (n = 61,017) in the matched data. Among true matches (mean age 66.4 years; men 58.4%), the median National Institutes of Health Stroke Scale score was 3 (interquartile range 1-7). When comparing baseline characteristics between true matches and false matches, no substantial difference was observed for any variable. CONCLUSION: We could establish big data on stroke by linking CRCS registry and HIRA records, using claims data without personal identifiers. We plan to conduct national stroke research and improve stroke care using the linked big database.

Perception Study of Traditional Korean Medical Students on the Medical Education Using the Dundee Ready Educational Environment Measure.

Background. In Korea, a few studies regarding traditional Korean medicine (TKM) education have been conducted. The aim of this study is to evaluate students' perceptions regarding TKM education in Korea and compare them with those of other countries using a quantitative scale, Dundee Ready Educational Environment Measure (DREEM). Materials and Methods. We conducted a survey using DREEM in a TKM college. Totally, 325 students responded to this survey and we performed the descriptive statistics of scores in all items, subscales, and total. Additionally, subgroup comparisons according to gender, school year, and academic achievement were analyzed. Results. Mean overall DREEM score was 94.65 out of 200, which is relatively low compared to previous studies. Particularly, perceptions regarding subscales of learning, atmosphere, and self-perceptions were interpreted as problematic. There was no statistically significant difference between genders in spite of some differences among groups based on school year or academic achievement. Conclusions. We could examine students' perceptions regarding TKM education at a TKM college using DREEM for which validity and reliability were verified. TKM education was perceived relatively poor, but these quantitative indicators suggested which parts of education need improvement. We expect DREEM to be used widely in TKM or traditional medical education field.